Prediction of indoor air exposure from outdoor air quality using an artificial neural network model for inner city commercial buildings

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Challoner A., Pilla F., Gill L.W., Prediction of indoor air exposure from outdoor air quality using an artificial neural network model for inner city commercial buildings, International Journal of Environmental Research and Public Health, 12, 2015, 15233 - 15253

Abstract:

NO
2
and particulate matter are the air
pollutants of
most
concern in Ireland
, with
possible links
to
the
high
er
respiratory and cardiovascular mortality and morbidity rates
found in the country
compared to the rest of Europe. Currentl
y, air quality limits in Europe
only cover outdoor environments yet the quality of indoor air is a
n essential determinant of
a person
’
s well
-
being, especially since the average person spends more than 90% of their
time indoors.
T
he modelling con
ducted in this research aims to
provid
e
a
framework
for
epidemiological studies
by the
use
of
publically
availab
le data from fixed outdoor
monitoring
stations to predict indoor air quality more accurately. Predictions are made using two modelling
techniques,
the
Personal
-
exposure Activity Location Model (PALM)
,
to predict outdoor air
quality at a particular b
uilding, and Artificial Neural Networks, to model the indoor/outdoor
relationship of the building. This
joint approach has been used to predict
indoor air
concentrations
for three
inner city commercial buildings in Dublin
,
where diurnal monitoring
of indoo
r and outdoor had been carried out on
site.
T
his
modelling
methodology
has been
shown to provide reasonable predictions of average NO
2
indoor air quality compared to
the
monitored data
,
but did not perform well in the prediction of indoor PM
2.
5
concentrati
ons.
Hence, this approach could
be used to determine
NO
2
exposures more rigorously
of those
who work and/or live in the city centre
,
which can then be linked to
potential health impacts
.